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TL;DR - Apache Airflow
- Apache Airflow is a workflow orchestration platform for programmatically authoring and scheduling data pipelines
- It defines workflows as code using Python DAGs, with built-in monitoring and retry capabilities
- Free and open-source, with managed versions from Astronomer starting at $500/month
Pricing: Paid only
Best for: Enterprises & pros
Score: 86/100
Pros & Cons
Pros
- Best workflow orchestration
- Python-based DAGs
- Large community
- Many operators
- Good monitoring
Cons
- Resource intensive
- Complex setup
- Learning curve
- Debugging difficult
- Scaling needs work
Key Features
Workflow orchestrationDAGsSchedulingMonitoringOperatorsOpen source
Pricing Plans
Free TrialOpen Source
Usage-based pricing
- Full functionality
- Community support
- Self-managed infrastructure
- Apache license
Most Popular
AWS MWAA
$0.49/hour
Managed service
- Fully managed
- Auto-scaling
- AWS integration
- Pay-as-you-go
Astronomer
$0.35/hour
Managed platform
- Expert support
- Easy deployment
- Monitoring included
- Team features
About Apache Airflow
By Toolradar Team·
Airflow orchestrates complex data pipelines by letting you define workflows as code. Instead of scheduling scripts with cron and hoping they run in order, you write Python that describes dependencies between tasks.
The scheduler handles retries, parallelism, and backfills automatically. A web interface shows what's running, what failed, and where data is in your pipeline. Alerts notify you when things go wrong.
Data engineering teams treat Airflow as essential infrastructure. When pipelines grow from a few scripts to hundreds of interconnected jobs, Airflow provides the structure that keeps everything manageable.
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Apache Airflow FAQ
Is Apache Airflow free?
Yes, Airflow is open source and free under Apache 2.0 license. You can self-host it at no cost. Managed services like Astronomer and MWAA (AWS) are paid options.
What is Apache Airflow?
Airflow is a workflow orchestration platform. Define, schedule, and monitor data pipelines as code using Python. Created at Airbnb, now an Apache project used by thousands of companies.
What is Airflow used for?
Scheduling and monitoring ETL jobs, data pipelines, ML workflows, and automated tasks. Define dependencies between tasks using DAGs (Directed Acyclic Graphs).
Airflow vs Prefect?
Both are workflow orchestrators. Airflow is more mature with larger ecosystem. Prefect has better developer experience and easier setup. Airflow for enterprise; Prefect for modern data teams.
How do you deploy Airflow?
Self-host with Docker/Kubernetes or use managed services. Astronomer provides commercial support. AWS MWAA and Google Cloud Composer offer managed Airflow. Complex to self-manage at scale.
Source: airflow.apache.org